Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "115" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459998 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.159219 | -0.803016 | -1.062709 | 0.006323 | -1.040713 | -0.555564 | -0.434030 | 1.863560 | 0.5547 | 0.5830 | 0.3847 | nan | nan |
| 2459997 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.196049 | -0.906359 | -0.893236 | 0.094671 | -0.910758 | -0.686640 | -0.348837 | 2.510782 | 0.5717 | 0.6006 | 0.3881 | nan | nan |
| 2459996 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.266542 | -0.564593 | -1.402808 | 0.193847 | -0.927642 | -0.631031 | 0.059612 | -0.464520 | 0.5701 | 0.5966 | 0.3986 | nan | nan |
| 2459995 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.971540 | -0.735542 | -1.303141 | 0.130178 | -0.733890 | -0.896314 | 0.219200 | -1.041447 | 0.5626 | 0.5926 | 0.3904 | nan | nan |
| 2459994 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.097709 | -0.953339 | -1.075795 | 0.034487 | -0.543591 | -0.621276 | 0.075973 | -0.433056 | 0.5622 | 0.5916 | 0.3827 | nan | nan |
| 2459993 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.013634 | -0.907769 | -0.808846 | 0.215902 | -0.157646 | -0.783147 | -0.514517 | -0.862712 | 0.5507 | 0.6063 | 0.3985 | nan | nan |
| 2459991 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.120315 | -0.940009 | -0.935750 | 0.223803 | -0.087917 | -0.789223 | -0.541442 | -0.882749 | 0.5648 | 0.5871 | 0.3905 | nan | nan |
| 2459990 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.851044 | -0.692983 | -0.906119 | 0.318676 | -0.204630 | -0.699669 | -0.697378 | -1.169097 | 0.5588 | 0.5853 | 0.3881 | nan | nan |
| 2459989 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.090617 | -0.791637 | -0.602668 | 0.237523 | -0.324292 | -1.020981 | -0.740949 | -1.242964 | 0.5567 | 0.5866 | 0.3906 | nan | nan |
| 2459988 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.295651 | -0.776698 | -1.004520 | 0.339399 | -0.184589 | -0.325619 | -0.487967 | -0.400536 | 0.5587 | 0.5877 | 0.3842 | nan | nan |
| 2459987 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.215265 | -0.932927 | -1.011617 | 0.061019 | -0.839551 | -0.822781 | -0.740969 | -1.534922 | 0.5698 | 0.5983 | 0.3803 | nan | nan |
| 2459986 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.923456 | -0.882208 | -1.081214 | 0.204322 | -0.517550 | -0.269250 | 1.142238 | -1.459637 | 0.5942 | 0.6263 | 0.3361 | nan | nan |
| 2459985 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.338845 | -0.921099 | -1.137286 | -0.015260 | -0.829330 | -0.965309 | -0.207489 | -1.171685 | 0.5701 | 0.5982 | 0.3880 | nan | nan |
| 2459984 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.238056 | -1.155000 | -1.056502 | 0.123198 | -1.181835 | -0.834114 | -0.443233 | -1.095105 | 0.5856 | 0.6163 | 0.3708 | nan | nan |
| 2459983 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.969535 | -0.961716 | -1.010037 | -0.393614 | -0.478280 | -0.852284 | 0.263930 | -0.305256 | 0.6029 | 0.6382 | 0.3148 | nan | nan |
| 2459982 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.597987 | -0.855663 | -0.801641 | -0.379774 | -0.978376 | -1.312263 | -0.337006 | -0.767906 | 0.6418 | 0.6591 | 0.3015 | nan | nan |
| 2459981 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.859638 | -0.673350 | -0.989580 | -0.306161 | 0.011489 | -1.374243 | -0.301165 | 0.052414 | 0.5644 | 0.5901 | 0.3833 | nan | nan |
| 2459980 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.974686 | -0.795298 | -1.173975 | -0.720638 | -0.664573 | -1.430517 | -0.337539 | -0.737074 | 0.6145 | 0.6369 | 0.3148 | nan | nan |
| 2459979 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.844971 | -0.749411 | -1.023356 | -0.679344 | 0.025680 | -1.600642 | -0.822316 | -0.599973 | 0.5587 | 0.5889 | 0.3831 | nan | nan |
| 2459978 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.002611 | -0.674358 | -0.981317 | -0.536815 | -0.140571 | -1.246004 | -1.055728 | -0.214099 | 0.5580 | 0.5840 | 0.3903 | nan | nan |
| 2459977 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.886834 | -0.696915 | -1.110212 | -0.697642 | -0.670045 | -1.795578 | -0.793636 | -0.692242 | 0.5208 | 0.5474 | 0.3486 | nan | nan |
| 2459976 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.878963 | -0.766509 | -1.134204 | -0.621710 | -0.067938 | -1.321564 | -0.017460 | -0.221555 | 0.5663 | 0.5925 | 0.3822 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | nn Temporal Discontinuties | 1.863560 | -1.159219 | -0.803016 | -1.062709 | 0.006323 | -1.040713 | -0.555564 | -0.434030 | 1.863560 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | nn Temporal Discontinuties | 2.510782 | -1.196049 | -0.906359 | -0.893236 | 0.094671 | -0.910758 | -0.686640 | -0.348837 | 2.510782 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | nn Power | 0.193847 | -1.266542 | -0.564593 | -1.402808 | 0.193847 | -0.927642 | -0.631031 | 0.059612 | -0.464520 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | ee Temporal Discontinuties | 0.219200 | -0.971540 | -0.735542 | -1.303141 | 0.130178 | -0.733890 | -0.896314 | 0.219200 | -1.041447 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | ee Temporal Discontinuties | 0.075973 | -1.097709 | -0.953339 | -1.075795 | 0.034487 | -0.543591 | -0.621276 | 0.075973 | -0.433056 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | nn Power | 0.215902 | -1.013634 | -0.907769 | -0.808846 | 0.215902 | -0.157646 | -0.783147 | -0.514517 | -0.862712 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | nn Power | 0.223803 | -1.120315 | -0.940009 | -0.935750 | 0.223803 | -0.087917 | -0.789223 | -0.541442 | -0.882749 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | nn Power | 0.318676 | -0.692983 | -0.851044 | 0.318676 | -0.906119 | -0.699669 | -0.204630 | -1.169097 | -0.697378 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | nn Power | 0.237523 | -0.791637 | -1.090617 | 0.237523 | -0.602668 | -1.020981 | -0.324292 | -1.242964 | -0.740949 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | nn Power | 0.339399 | -0.776698 | -1.295651 | 0.339399 | -1.004520 | -0.325619 | -0.184589 | -0.400536 | -0.487967 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | nn Power | 0.061019 | -1.215265 | -0.932927 | -1.011617 | 0.061019 | -0.839551 | -0.822781 | -0.740969 | -1.534922 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | ee Temporal Discontinuties | 1.142238 | -0.882208 | -0.923456 | 0.204322 | -1.081214 | -0.269250 | -0.517550 | -1.459637 | 1.142238 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | nn Power | -0.015260 | -0.921099 | -1.338845 | -0.015260 | -1.137286 | -0.965309 | -0.829330 | -1.171685 | -0.207489 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | nn Power | 0.123198 | -1.238056 | -1.155000 | -1.056502 | 0.123198 | -1.181835 | -0.834114 | -0.443233 | -1.095105 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | ee Temporal Discontinuties | 0.263930 | -0.969535 | -0.961716 | -1.010037 | -0.393614 | -0.478280 | -0.852284 | 0.263930 | -0.305256 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | ee Temporal Discontinuties | -0.337006 | -0.597987 | -0.855663 | -0.801641 | -0.379774 | -0.978376 | -1.312263 | -0.337006 | -0.767906 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | nn Temporal Discontinuties | 0.052414 | -0.673350 | -0.859638 | -0.306161 | -0.989580 | -1.374243 | 0.011489 | 0.052414 | -0.301165 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | ee Temporal Discontinuties | -0.337539 | -0.795298 | -0.974686 | -0.720638 | -1.173975 | -1.430517 | -0.664573 | -0.737074 | -0.337539 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | ee Temporal Variability | 0.025680 | -0.844971 | -0.749411 | -1.023356 | -0.679344 | 0.025680 | -1.600642 | -0.822316 | -0.599973 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | ee Temporal Variability | -0.140571 | -0.674358 | -1.002611 | -0.536815 | -0.981317 | -1.246004 | -0.140571 | -0.214099 | -1.055728 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | ee Temporal Variability | -0.670045 | -0.886834 | -0.696915 | -1.110212 | -0.697642 | -0.670045 | -1.795578 | -0.793636 | -0.692242 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 115 | N11 | not_connected | ee Temporal Discontinuties | -0.017460 | -0.766509 | -0.878963 | -0.621710 | -1.134204 | -1.321564 | -0.067938 | -0.221555 | -0.017460 |